Search

A global in-situ data set for validation of ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in-situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient and total suspended matter. Data were obtained from multi-project archives acquired via open internet services, or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 151,673 rows, with each row representing a unique station in space and time (cf 136,250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68,641 (cf 59,781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.pangaea.de/10.1594/PANGAEA.941318 (Valente et al., 2022)., SI

Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data.

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS), and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales, their role in marine biogeochemistry, the global carbon cycle, the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean, and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration, since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency, the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA), and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Felyx is currently under development and is the latest evolution of a generalised High Resolution Diagnostic Data Set system funded by ESA. It draws on previous prototype developments and experience in the GHRSST, Medspiration, GlobColour and GlobWave projects. In this paper, we outline the design and implementation of the system, and illustrate using the Ocean Colour demonstration activities. Felyx is fundamentally a tool to facilitate the analysis of EO data: it is being developed by IFREMER, PML and Pelamis. It will be free software written in python and javascript. The aim is to provide Earth Observation data producers and users with an opensource, flexible and reusable tool to allow the quality and performance of data streams from satellite, in situ and model sources to be easily monitored and studied. New to this project, is the ability to establish and incorporate multi-sensor match-up database capabilities. The systems will be deployable anywhere and even include interaction mechanisms between the deployed instances. The primary concept of Felyx is to work as an extraction tool. It allows for the extraction of subsets of source data over predefined target areas(which can be static or moving). These data subsets, and associated metrics, can then be accessed by users or client applications either as raw files or through automatic alerts. These data can then be used to generate periodic reports or be used for statistical analysis and visualisation through a flexible web interface. Felyx can be used for subsetting, the generation of statistics, the generation of reports or warnings/alerts, and in-depth analyses, to name a few. There are many potential applications but important uses foreseen are: * monitoring and assessing the quality of Earth observations (e.g. satellite products and time series) through statistical analysis and/or comparison with other data sources * assessing and inter-comparing geophysical inversion algorithms * observing a given phenomenon, collecting and cumulating various parameters over a defined area * crossing different sources of data for synergy applications The services provided by felyx will be generic, deployable at users own premises, and flexible allowing the integration and development of any kind of parameters. Users will be able to operate their own felyx instance at any location, on datasets and parameters of their own interest, and the various instances will be able to interact with each other, creating a web of felyx systems enabling aggregation and cross comparison of miniProds and metrics from multiple sources. Initially two instances will be operated simultaneously during a 6 months demonstration phase, at IFREMER – on sea surface temperature and ocean waves datasets – and PML – on ocean colour.

MODIS white sky surface albedos are compared with similar products generated on the basis of the MISR surface BRF model parameters available for year 2005. The analysis is achieved using global scale statistics in order to characterize the broad patterns of these two independent albedo datasets. The results obtained in Taberner etal. (2010) have shown that robust statistics can be established and that both datasets are highly correlated. As a result, the slight but consistent biases and trends identified in this paper, derived from statistics obtained on a global basis, should be considered sufficiently reliable to merit further investigation. The present paper reports on the zonal and seasonal mean differences retrieved from the analysis of the MODIS and MISR surface albedo broadband products. The MISR-MODIS differences exhibit a systematic positive bias or offset in the range 0.01-0.03 depending on the spectral domain of interest. Results obtained in the visible domain exhibit a well-marked and very consistent meridional trend featuring a `smile effect' such that the MISR-MODIS differences reach maxima at the highest latitudes in both hemispheres. The analysis of seasonal variations observed in MISR and MODIS albedo products reveals that, in the visible domain, the MODIS albedos generate weaker seasonal changes than MISR and that the differences increase polewards from the equatorial regions. A detailed investigation of MODIS and MISR aerosol optical depth retrievals suggests that this large scale meridional trend is probably not caused by differences in the aerosol load estimated by each instrument. The scale and regularity of the meridional trend suggests that this may be due to the particular sampling regime of each instrument in the viewing azimuthal planes and/or approximations in the atmospheric correction processes. If this is the case, then either MODIS is underestimating, or MISR overestimating, the surface anisotropy, or both., JRC.H.7-Climate Risk Management

Operational weather geostationary satellites have acquired data for more than two decades and offer thus the possibility to generate long time series of Essential Climate Variables like surface albedo as suggested by GCOS. This paper investigates the possibility to generate consistent global, to the exception of the polar regions, surface broadband albedo product from these satellites. In this context, the paper addresses two specific issues. Firstly, the spatial consistency of surface albedo derived from five different geostationary satellites is examined in detail. Secondly, this product is compared with the equivalent MODIS one to define the temporal consistency between surface albedo derived with old geostationary instruments and technologically advanced radiometers like MODIS. The analysis of the surface albedo products has revealed a good agreement between the products derived from the various geostationary satellites. Comparison of this global product with the one routinely derived from MODIS shows that on the average, the mean relative difference between these two data sets agree within 10%. These first encouraging results open therefore a new avenue for the exploitation of the archived data for the generation of long time series, covering the last 25 years or so, of global surface albedo from geostationary weather satellites.

This paper discusses the accuracy of the operational Medium Resolution Imaging Spectrometer (MERIS) Level 2 land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The FAPAR value is estimated from daily MERIS spectral measurements acquired at the top-of-atmosphere, using a physically based approach. The products are operationally available at the reduced spatial resolution, i.e. 1.2 km, and can be computed at the full spatial resolution, i.e. at 300 m, from the top-of-atmosphere MERIS data by using the same algorithm. The quality assessment of the MERIS FAPAR products capitalizes on the availability of five years of data acquired globally. The actual validation exercise is performed in two steps including, first, an analysis of the accuracy of the FAPAR algorithm itself with respect to the spectral measurements uncertainties and, second, with a direct comparison of the FAPAR time series against ground-based estimations as well as similar FAPAR products derived from other optical sensor data. The results indicate that the impact of top-of-atmosphere radiance uncertainties on the operational MERIS FAPAR products accuracy is expected to be at about 5–10% and the agreement with the ground-based estimates over different canopy types is achieved within ± 0.1.

The phenology of vegetation, which describes the seasonal evolution of plants, can be effectively monitored from space. This approach offers important advantages compared to field observations, as quantitative information can be derived for any location worldwide over a number of years, thereby offering a consistent overview of the fate of the observed biomes and their relations with the climate and the environment. This manuscript describes a method to define the start, end, and length of ‘growing seasons’ based on the statistical analysis of time series of the biogeophysical quantity known as the Fraction of Absorbed Photosynthetically Active Radiation derived from an analysis of SeaWiFS data. Results are discussed for various biomes.

A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594/PANGAEA.854832 (Valente et al., 2015).

The capability of the non-linear Rahman–Pinty–Verstraete (RPV) model to 1) accurately fit a large variety of Bidirectional Reflectance Factor (BRF) fields and 2) return parameter values of interest for land surface applications motivate the development of a computer efficient inversion package. The present paper describes such a package based on the 3 and 4 parameter versions of the RPV model. This software environment implements the adjoint code, generated using automatic differentiation techniques, of the cost function. This cost function itself balances two main contributions reflecting 1) the a priori knowledge on the model parameter values and, 2) BRF uncertainties together with the requirement to minimize the mismatch between the measurements and the RPV simulations. The individual weights of these contributions is specified notably via covariance matrices of the uncertainties in the a priori knowledge on the model parameters and the observations. This package also reports on the probability density functions of the retrieved model parameter values that thus permit the user to evaluate the a posteriori uncertainties on these retrievals. This is achieved by evaluating the Hessian of the cost function at its minimum. Results from a variety of tests are shown in order to document and analyze software performance against complex synthetic BRF fields simulated by radiation transfer models as well as against actual MISR-derived surface BRF products.

This paper describes the evaluation and performance of the Medium Resolution Imaging Spectrometer (MERIS) Global Vegetation Index (MGVI) algorithm that is implemented in the MERIS ground segment as the primary land surface product. MGVI output values represent the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) which acts as an indicator of the presence and state of the vegetation canopy. The retrieval algorithm was optimized to be insensitive to the overlying atmosphere, the underlying soil as well as angular effects, using radiative transfer models. This physically based approach for retrieving land biophysical parameters can be extended to a series of sensors; the resulting algorithms are designed to deliver similar geophysical products that are directly comparable and ultimately generate long time series of FAPAR. After presenting the MGVI algorithm, we analyze actual results by inter-comparing the FAPAR values derived from MERIS to similar products derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) that have been generated at the European Commission Joint Research Centre (EC-JRC). The FAPAR products derived from MERIS and SeaWiFS are compared on several dates in 2002 when both instruments simultaneously observed the same geographical regions. The consistency and temporal continuity of the products are then evaluated by comparing the time series of FAPAR values over local sites in Europe during the year 2003 from the two instruments. Finally, multi-annual FAPAR time series obtained by merging MERIS and SeaWiFS products demonstrate the feasibility of monitoring the state of land surfaces with FAPAR products before and after the European drought event in 2003.

The state of terrestrial vegetation has been monitored using remote sensing data for decades. Information was often derived from empirical tools, like vegetation indices, which are very sensitive to perturbations and often depend on the spectral properties of the sensor. Advances in the understanding of radiation transfer and the availability of higher performance instruments have stimulated the development of a new generation of geophysical products poised to provide reliable, accurate information on the state and evolution of terrestrial environments. A series of optimized algorithms have been developed for documenting biophysical activities, using a physically based approach (specifically, to estimate Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) for various instruments. The outline of the methodology will be summarized and the results from an application conducted with SeaWiFS data will be presented.

New satellite instruments have been delivering a wealth of information regarding land surface albedo. This basic quantity describes what fraction of solar radiation is reflected from the earth’s surface. However, its concept and measurements have some ambiguity resulting from its dependence on the incidence angles of both the direct and diffuse solar radiation. At any time of day, a surface receives direct radiation in the direction of the sun, and diffuse radiation from the various other directions in which it may have been scattered by air molecules, aerosols, and cloud droplets. This contribution proposes a complete description of the distribution of incident radiation with angles, and the implications in terms of surface albedo are given in a mathematical form, which is suitable for climate models that require evaluating surface albedo many times. The different definitions of observed albedos are explained in terms of the coupling between surface and atmospheric scattering properties. The analytical development in this paper relates the various quantities that are retrieved from orbiting platforms to what is needed by an atmospheric model. It provides a physically simple and practical approach to evaluation of land surface albedo values at any condition of sun illumination irrespective of the current range of surface anisotropic conditions and atmospheric aerosol load. The numerical differences between the various definitions of albedo for a set of typical atmospheric and surface scattering conditions are illustrated through numerical computation.

15 pages, 7 figures, 2 tables, Migrations between different habitats are key events in the lives of many organisms. Such movements involve annually recurring travel over long distances usually triggered by seasonal changes in the environment. Often, the migration is associated with travel to or from reproduction areas to regions of growth. Young anadromous Atlantic salmon (Salmo salar) emigrate from freshwater nursery areas during spring and early summer to feed and grow in the North Atlantic Ocean. The transition from the freshwater ('parr') stage to the migratory stage where they descend streams and enter salt water ('smolt') is characterized by morphological, physiological and behavioural changes where the timing of this parr-smolt transition is cued by photoperiod and water temperature. Environmental conditions in the freshwater habitat control the downstream migration and contribute to within- and among-river variation in migratory timing. Moreover, the timing of the freshwater emigration has likely evolved to meet environmental conditions in the ocean as these affect growth and survival of the post-smolts. Using generalized additive mixed-effects modelling, we analysed spatio-temporal variations in the dates of downstream smolt migration in 67 rivers throughout the North Atlantic during the last five decades and found that migrations were earlier in populations in the east than the west. After accounting for this spatial effect, the initiation of the downstream migration among rivers was positively associated with freshwater temperatures, up to about 10 °C and levelling off at higher values, and with sea-surface temperatures. Earlier migration occurred when river discharge levels were low but increasing. On average, the initiation of the smolt seaward migration has occurred 2.5 days earlier per decade throughout the basin of the North Atlantic. This shift in phenology matches changes in air, river, and ocean temperatures, suggesting that Atlantic salmon emigration is responding to the current global climate changes., This study is part of the Norwegian Research Council project nº 183989/S30. J.O. acknowledges additional funding from Norwegian Water and Energy Directorate (NVE).

This article demonstrates the use of spectral decomposition for analyzing the spectral response of different semiarid vegetation species found throughout Mediterranean Europe. Using this technique, it is possible to decompose a spectral data set into a smaller number of significant factors that represent the key variables affecting vegetation spectral response. The results presented here show how spectral decomposition can be used to determine the intrinsic number and identity of the significant factors affecting the multispectral response. For the dataset investigated here, which comprises field spectral recorded over 1130 wavelengths, using a GER single field-of-view IRIS (SIRIS) spectroradiometer, it was found that a combination of just four factors was responsible for the majority of spectral variance. Interpretation of these factors was carried out by graphical analysis stepwise regeneration of the original spectra, and correlation with biophysical data. Considering the identity of these factors, it was found that the second most significant factor (factor 2) was strongly related to the proportion of directly irradiated green leaves within the field-of-view of the spectradiometer. In addition, it was found that the fourth most significant factor (factor 4) provided a good summary of the spectral response of the different samples in the region of strong chlorophyll absorption. This demonstrates the possibility of using spectral decomposition techniques, particularly in environments dominated by spectrally similar vegetation classes, to model the mixed spectral population as mixtures of fundamental biophysical parameters rather than as mixtures of the classes themselves.

[1] The two-stream model parameters and associated uncertainties retrieved by inversion against MODIS broadband visible and near-infrared white sky surface albedos were discussed in a companion paper. The present paper concentrates on the partitioning of the solar radiation fluxes delivered by the Joint Research Centre Two-stream Inversion Package (JRC-TIP). The estimation of the various flux fractions related to the vegetation and the background layers separately capitalizes on the probability density functions of the model parameters discussed in the companion paper. The propagation of uncertainties from the observations to the model parameters is achieved via the Hessian of the cost function and yields a covariance matrix of posterior parameter uncertainties. This matrix is propagated to the radiation fluxes via the model's Jacobian matrix of first derivatives. Results exhibit a rather good spatiotemporal consistency given that the prior values on the model parameters are not specified as a function of land cover type and/or vegetation phenological states. A specific investigation based on a scenario imposing stringent conditions of leaf absorbing and scattering properties highlights the impact of such constraints that are, as a matter of fact, currently adopted in vegetation index approaches. Special attention is also given to snow-covered and snow-contaminated areas since these regions encompass significant reflectance changes that strongly affect land surface processes. A definite asset of the JRC-TIP lies in its capability to control and ultimately relax a number of assumptions that are often implicit in traditional approaches. These features greatly help us understand the discrepancies between the different data sets of land surface properties and fluxes that are currently available. Through a series of selected examples, the inverse procedure implemented in the JRC-TIP is shown to be robust, reliable, and compliant with large-scale processing requirements. Furthermore, this package ensures the physical consistency between the set of observations, the two-stream model parameters, and radiation fluxes. It also documents the retrieval of associated uncertainties.

[1] This contribution illustrates results from a large-scale application of the Joint Research Centre Two-stream Inversion Package (JRC-TIP), using MODIS broadband visible and near-infrared white sky surface albedos as inputs. The discussion focuses on products (based on the mean and one-sigma values of the probability distribution functions (PDFs)) obtained during the summer and winter. This paper discusses the retrieved model parameters including the effective leaf area index (LAI), the background brightness, and the scattering efficiency of the vegetation elements. The similarity between the derived LAI seasonal maps and earlier distributions of this variable comforts us in the quality of the albedo products as well as in the ability of the JRC-TIP to interpret the latter meaningfully. The opportunity to generate global maps of new products, such as the background albedo, underscores the advantages of using state of the art algorithmic approaches capable of fully exploiting accurate satellite remote sensing data sets. The detailed analyses of the retrieval uncertainties highlight the central role and contribution of the LAI, the main process parameter to interpret radiation transfer observations over vegetated surfaces. The estimation of the radiation fluxes that are absorbed, transmitted, and scattered by the vegetation layer and its background is achieved on the basis of the retrieved PDFs of the model parameters. Results from this latter step are discussed in a companion paper.

[1] The fraction of radiation absorbed in the canopy depends on the amount and angular distribution of the solar irradiance reaching the top of the canopy as well as the fraction of this irradiance that is transmitted through the canopy gaps and reflected back to the vegetation by the background. This contribution shows that the presence of snow on forest floors enhances the fraction of absorbed Photosynthetically Active Radiation (PAR). A global analysis of satellite-derived products reveals that this enhancement affects evergreen and deciduous forests of the boreal zone. This snow-related effect may usefully contribute to the photosynthesis process in evergreen forests especially during spring time when radiation conditions are marginal but other physiological constraints (such as temperature) permit the necessary biochemical functions to take place.

[1] The broadband white sky surface albedo (bihemispherical reflectance) products available from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared at regional and continental scales with similar products generated from the Multiangle Imaging Spectroradiometer (MISR) land surface bidirectional reflectance factor (BRF) parameters. This paper describes the methodology applied to derive MISR white sky albedos over four spectral broadbands of interest, namely, 0.3–0.7 μm, 0.4–1.1 μm, 0.7–3.0 μm, and 0.3–3.0 μm, as well as an evaluation of the strategy adopted to compare the MODIS and MISR products. The results are very encouraging since the two data sets show very good statistical agreement over large areas and over a full year of measurements, despite the many differences that exist in the suite of algorithms applied to retrieve these surface quantities from each of these instruments separately.

The main goal of this study is to help bridge the gap between available remote sensing products and large-scale global climate models. We present results from the application of an inversion method conducted using both MODIS and MISR derived broadband visible and near-infrared surface albedo products. This contribution is an extension of earlier efforts to optimally retrieve land surface fluxes and associated two-stream model parameters (pinty etal., 2007). It addresses complex geophysical scenarios involving snow occurrence in mid and high-latitude evergreen and deciduous forest canopy systems. The detection of snow during the winter and spring seasons is based on the MODIS snow product. This information is used by our package to adapt the prior values, specifically the maximum likelihood and width of the 2-D probability density functions (PDF) characterizing the background conditions of the forest floor. Our results (delivered as a Gaussian approximation of the PDFs of the retrieved model parameter values and radiant fluxes) illustrate the capability of the inversion package to retrieve meaningful land vegetation fluxes and associated model parameters during the year, despite the rather high temporal variability in the input products, in large part due to the occurence of snow events. As a matter of fact, most of this temporal variability, as well as the small differences between the MODIS and MISR broadband albedos, appear to be largely captured by the albedo of the forest canopy background., JRC.H.5-Land Resources Management

[1] We present a computer-efficient software package enabling us to assimilate operational remote-sensing flux products into a state-of-the-art two-stream radiation transfer scheme suitable for climate models. This package implements the adjoint and Hessian codes, generated using automatic differentiation techniques, of a cost function balancing (1) the deviation from the a priori knowledge on the model parameter values and (2) the misfit between the observed remote-sensing fluxes and the two-stream model simulations. The individual weights of these contributions are specified notably via covariance matrices of the uncertainties in the a priori knowledge on the model parameters and the measurements. The proposed procedure delivers a Gaussian approximation of the PDFs of the retrieved model parameter values. The a posteriori covariance matrix is further exploited to evaluate, in turn, the posterior probability density functions of the radiant fluxes simulated by the two-stream model, including those that are not measured, for example, the fraction of radiation absorbed in the ground. Applications are conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) broadband surface albedo products. It turns out that the differences between these two albedo sets may translate into discernible signatures on some retrieved model parameters. Meanwhile, adding the Joint Research Centre (JRC)-Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Sea-viewing Wide Field-of-view Sensor (SeaWiFS) products into the measurements yields a significant reduction of uncertainties. Results from these applications indicate that the products retrieved from the two-stream inversion procedure (1) exhibit much less variability than those generated by the operational algorithms for the LAI and FAPAR, and (2) are in good agreement with the available ground-based estimates.

This paper discusses the validation of the operational Medium Resolution Imaging Spectrometer (MERIS) land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This biophysical variable acts as an indicator of the presence and state of the vegetation and it is currently estimated from MERIS data at both reduced and full resolution using a physically-based approach. The quality of the MERIS FAPAR products, derived from the MERIS Global Vegetation Index (MGVI) algorithm, capitalizes on the availability of MERIS data since June 2002. The validation protocol to assess the accuracy of FAPAR product includes (1) the estimates of theoretical uncertainties (versus the algorithm formulae and instrument calibration performance), (2) the assessment of the performance for detecting various events (verisimilitude) against the time-series of well-known land surfaces and (3) the direct comparisons of the FAPAR MERIS values to similar products generated by other independent sensors, like the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the MODerate Resolution Imaging Spectroradiometer (MODIS), and against ground-estimates of FAPAR performed over various vegetation types.

International audience; The Radiation Transfer Model Intercomparison (RAMI) initiative benchmarks canopy reflectance models under well-controlled experimental conditions. Launched for the first time in 1999, this triennial community exercise encourages the systematic evaluation of canopy reflectance models on a voluntary basis. The first phase of RAMI focused on documenting the spread among radiative transfer (RT) simulations over a small set of primarily 1-D canopies. The second phase expanded the scope to include structurally complex 3-D plant architectures with and without background topography. Here sometimes significant discrepancies were noted which effectively prevented the definition of a reliable ''surrogate truth,'' over heterogeneous vegetation canopies, against which other RT models could then be compared. The present paper documents the outcome of the third phase of RAMI, highlighting both the significant progress that has been made in terms of model agreement since RAMI-2 and the capability of/need for RT models to accurately reproduce local estimates of radiative quantities under conditions that are reminiscent of in situ measurements. Our assessment of the self-consistency and the relative and absolute performance of 3-D Monte Carlo models in RAMI-3 supports their usage in the generation of a ''surrogate truth'' for all RAMI test cases. This development then leads (1) to the presentation of the ''RAMI Online Model Checker'' (ROMC), an open-access web-based interface to evaluate RT models automatically, and (2) to a reassessment of the role, scope, and opportunities of the RAMI project in the future.

[1] This paper discusses the quality and the accuracy of the Joint Research Center (JRC) fraction of absorbed photosynthetically active radiation (FAPAR) products generated from an analysis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The FAPAR value acts as an indicator of the presence and state of the vegetation and it can be estimated from remote sensing measurements using a physically based approach. The quality of the SeaWiFS FAPAR products assessed in this paper capitalizes on the availability of a 6-year FAPAR time series over the full globe. This evaluation exercise is performed in two phases involving, first, an analysis of the verisimilitude of the FAPAR products under documented environmental conditions and, second, a direct comparison of the FAPAR values with ground-based estimations where and when the latter are available. This second phase is conducted following a careful analysis of problems arising for performing such a comparison. This results in the grouping of available field information into broad categories representing different radiative transfer regimes. This strategy greatly helps the interpretation of the results since it recognizes the various levels of difficulty and sources of uncertainty associated with the radiative sampling of different types of vegetation canopies.

[1] The Radiation Transfer Model Intercomparison (RAMI) initiative is a community-driven exercise to benchmark the models of radiation transfer (RT) used to represent the reflectance of terrestrial surfaces. Systematic model intercomparisons started in 1999 as a self-organized, open-access, voluntary activity of the RT modeling community. The results of the first phase were published by Pinty et al. [2001]. The present paper describes the benchmarking protocol and the results achieved during the second phase, which took place during 2002. This second phase included two major components: The first one included a rerun of all direct-mode tests proposed during the first phase, to accommodate the evaluation of models that have been upgraded since, and the participation of new models into the entire exercise. The second component was designed to probe the performance of three-dimensional models in complex heterogeneous environments, which closely mimic the observations of actual space instruments operating at various spatial resolutions over forest canopy systems. Phases 1 and 2 of RAMI both confirm not only that a majority of the radiation transfer models participating in RAMI are in good agreement between themselves for relatively simple radiation transfer problems but also that these models exhibit significant discrepancies when considering more complex but nevertheless realistic geophysical scenarios. Specific recommendations are provided to guide the future of this benchmarking program (Phase 3 and beyond).

Temporal changes of terrestrial vegetation have traditionally been monitored using empirical remote sensing tools, which are sensitive to perturbations as well as to the spectral properties of the sensor. Advances in the understanding of radiation transfer theory, and the availability of higher performance modern instruments, have led to the development of physically-based inverse methods to derive biogeophysical products. Jointly, these developments allow the retrieval of reliable, accurate information on the state and evolution of terrestrial environments. A series of optimized algorithms has been developed to document biogeophysical variables, and in particular to estimate the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from a variety of optical instruments. As a result, monitoring managed (e.g., agriculture) or natural ecosystems will benefit from the availability of local, regional and global time series of remote sensing products such as FAPAR. This paper outlines the methodology and exhibits selected results in the form of temporal composites derived from the SeaWiFS sensor.

In order to effectively protect coastal ecosystems, objective information on the structural and functional characteristics of these systems must be obtained through regular monitoring. In recent years, more advanced technologies, such as satellite remote sensing, have arisen which are being utilized for the monitoring and management of coastal ecosystems in general, and vegetation in particular. This paper presents the results from monitoring the vegetation biomass of the Koycegiz Lagoon area, declared as a Specially Protected Area by the Turkish Government, using multi-temporal satellite imagery. Several problems need to be overcome when using imagery from different dates or from different sensors. These include; image registration, atmospheric variability, and, often, the lack of historical ground data. Furthermore, for monitoring purposes, techniques to overcome these problems should be robust and automatic allowing the database to be upgraded easily. The procedures we have developed include automatic registration (to subpixel accuracy), atmospheric normalization, and vegetation index (VI) calibration components. This was tested on multidate (1984, 1988, 1991, 1995 and 1996) LANDSAT-TM data. From this adjusted data set the performance of different vegetation indices, in this coastal environment was examined, and the vegetation trends analyzed.